The detection of closely-spaced objects using infrared sensors has numerous practical applications. For example, infrared sensors may be employed in observational astronomy for the detection and tracking of stars, planets, and other extraterrestrial objects. Infrared sensors may also be used for military applications, including, for example, the long range electro-optical detection, tracking, and discrimination of targets and decoys. Similarly, infrared detection may be used in the imaging and analysis of cells and particles for medical disclosed, for example, in U.S. Pat. No. 6,495,827 issued to Metcalf et al., and U.S. Pat. No. 6,433,333 issued to Howard.
Although desirable results have been achieved using prior art infrared imaging systems, there is room for improvement. For example, prior art infrared systems typically perform an iterative curve-fitting process to match a closely-spaced object waveform having unknown amplitudes and angle variables (amplitude, azimuth and elevation for each object) to an N object blob measured by the infrared sensor. Such iterative curve-fitting processes are typically computationally intensive, thereby increasing the size and weight of the computer needed to adequately perform the intensive computations. The cost to develop and deploy such infrared imaging systems typically increases with increased weight and complexity, particularly for space-based applications that require relatively expensive launch systems to be placed in orbit. Furthermore, direct approaches to resolving closing spaced objects (CSO) that involve deploying larger aperture, heavier infrared optical systems increases system cost and sensor payload weight, particularly for space-based applications that require relatively expensive launch systems to be placed in orbit. Therefore, there is a continuing impetus to reduce the size, weight, and complexity of such infrared imaging systems and system components to reduce development and deployment costs.